Extended model of shear modulus reduction for cohesive soils

Wong, John Kok Hee; Wong, Soon Yee; Wong, Kelvin

doi:10.1007/s11440-021-01398-0

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Figure4shows the effect of relative density on the dynamic properties of granular soils. The results show that with an increase in relative density, G/Gmax increases, and D decreases, aligning with prior research findings[60][61][62]. Further elaboration and comprehensive findings stemming from these tests have been disseminated through detailed publications by Bayat and Ghalandarzadeh[58,63].…”

supporting

confidence: 84%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

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Dynamic properties (i.e., shear modulus and damping ratio) of geomaterials play a vital role in civil engineering applications and are essential for reliable dynamic response analysis. This paper presents a novel approach for predicting the normalized shear modulus (G/Gmax) and damping ratio (D) of granular soils across a wide strain range using a Deep Neural Network (DNN) modeling strategy. Traditional methods for predicting these properties often rely on empirically derived relationships that may not capture the full complexity of granular soil behavior under varying strain conditions. A comprehensive dataset of shear modulus and damping ratio measurements from laboratory cyclic triaxial (CT) and resonant column (RC) tests conducted under various conditions is utilized. The dataset covers a wide range of strain levels, allowing for a more robust and versatile modeling approach. For predicting the G/Gmax and D of granular soils, a Deep Feed-Forward Neural Network (DFFNN) model was developed to learn the features from input data. The proposed model considers the influence of grading characteristics (Gravel Content, GC, median particle size, D50, Uniformity Coefficient, Cu, and Coefficient of Curvature, Cc), shear strain (\(\gamma\)), void ratio (e), mean effective confining pressure (\({\sigma ^{\prime}_m}\)), consolidation stress ratio (KC) and specimens’ preparation method (S-P) as input data. The empirical models (EMs) and three other intelligent techniques, namely Shallow Neural Network (SNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR) were used for comparison. The testing accuracy of the proposed DFFNN for predicting the G/Gmax and D was 0.9830 and 0.9396, respectively. The results demonstrate that the proposed DFFNN modeling strategy provides a highly accurate means of predicting G/Gmax and D for granular soils across a broad shear strain range. This method offers advantages over EMs by incorporating a data-driven approach that can adapt to the specific behavior of different granular soil types and loading conditions.

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supporting

confidence: 84%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

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“…The soil stiffness or shear modulus G at small to large strains generally results from the material properties of the soil (grain characteristics, plasticity, void ratio, cementation), the stress state or stress history (overconsolidation ratio, mean effective stress) and the dynamic loading characteristics (frequency, number of cycles, drained/undrained conditions). However, factors that significantly influence G are the engineering shear strain amplitude γ, soil plasticity index PI and effective stress σ ′ [8,9].…”

Section: Assessment Of Soil Stiffness At Small Strainsmentioning

confidence: 99%

Small Strain Shear Modulus of the Ljubljana Marsh Soil Measured with Resonant Column and Bender Elements under Isotropic and Anisotropic Stress Conditions

Jurček,

Pulko,

Maček

2024

Applied Sciences

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The increasing use of finite element analysis in modern infrastructure design emphasizes the importance of determining soil stiffness at small strains. This is usually represented by the normalized shear modulus degradation curve, which is crucial for accurate design. In the absence of specific measurements on the local soil, engineers often rely on empirical correlations and assume comparable behavior of soils with similar intrinsic properties. However, the application of this approach leads to uncertainties, especially for unique geological formations such as the soft cohesive soils of the Ljubljana Marsh. The main objective of this study was to determine the small strain shear modulus of Ljubljana Marsh soil with a plasticity index between 11 and 35%. Isotropic and anisotropic stress conditions were investigated as part of an extensive laboratory test program that included 45 bender element and 89 resonant column tests on 20 soil samples. By emphasizing the importance of measuring soil stiffness at small strains, this study not only provides reliable data for the development of the built environment in the Ljubljana Marsh and similar areas, but also underlines its necessity.

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“…Ottawa sand No. 20 This equation has a relatively large scatter indicating the difficulty of obtaining a correlation for the strain-dependent shear stiffness that would be reliable for a wide variety of granular soils. Therefore, a different approach can model the dynamic behavior of soil at larger strain levels.…”

Section: Soil Tested D50 [Mm] Cu [-] a [-] F(e) [-] N [-] Referencementioning

confidence: 99%

The Dynamic Properties of Sand under Torsion: A Literature Review

Ahmad

Ray

2023

Geotechnics

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Resonant column (RC) and the torsional simple shear (TOSS) tests have shown proven competency in acquiring precise and repeatable measurements regarding the shear modulus and damping ratio of soil. For most dynamic geotechnical problems, the shear modulus represents the stiffness of the soil, while the damping ratio describes energy dissipation. Many studies in the last few decades focused on developing the relevant equipment and investigating the effect of different soil properties on the dynamic behavior of soil. Researchers have introduced correlations to approximate this behavior without conducting dynamic torsional testing. Soil models (e.g., Ramberg-Osgood and Hardin-Drnevich) can simulate shear stress-strain curves after finding the curve-fitting parameters. Due to the complexity of dynamic behavior and its dependency on various factors in soils, the RO and HD equations help model the behavior more simply. This paper presents a literature review and evaluation of the studies, correlations, soil models, and parameters affecting the dynamic behavior of dry sand under torsion.

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Extended model of shear modulus reduction for cohesive soils

Cited by 4 publications

References 20 publications

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Small Strain Shear Modulus of the Ljubljana Marsh Soil Measured with Resonant Column and Bender Elements under Isotropic and Anisotropic Stress Conditions

The Dynamic Properties of Sand under Torsion: A Literature Review

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